Real-time data integration in information systems using stream processing for medical data

Abstract

Real-time data processing in medical information systems is becoming harder with the increase in data volume. Stream processing is a popular approach for real-time data processing, which can process large volumes of data including medical in a scalable manner. In some cases, medical data may not be available in real time because of privacy and security concerns. In this paper, we will explore the use of stream processing with static medical data using streaming platforms, Kafka, and Apache Spark. We will demonstrate how these platforms can be used to work with static data in streams and discuss the benefits and limitations of the approach. We also present a case study to illustrate the effectiveness and performance.